1. Field of Invention
The present invention relates generally to the optimization of strategies for collecting and recovering on delinquent debt accounts, and more particularly, to an automated system that uses predictive modeling to optimize the use of various collection resources on a portfolio of delinquent debt accounts, including for example credit card accounts.
2. Background of the Related Art
A significant portion of the debts that people incur are not repaid in a timely fashion. The term “debt” as used herein may refer to credit card debt, loan debts, unpaid bills, or a variety of other types of debt or credit obligation. A delinquent debt is any such debt that has not been repaid by its due date, or a debt in which one or more installment payments have been missed. Debt issuers typically employ various different methods to collect on these delinquent debts, either in full or in part.
Assume for purposes of example that a debtor stops making monthly payments on his credit card debt. Typically, the credit card company will use various collection methods, such as letters and phone calls, to encourage the customer to pay. However, once the account is 180 days overdue, it attains the legal definition of a non-performing debt and must be charged off. Subsequent efforts to collect the debt are known as “recoveries.” At this point, the credit card company may continue to work the debt in-house, or may elect to sell the debt to a contingency collection agency.
Such delinquent debts are often sold for pennies on the actual dollar value of the debt. A variety of existing analytical methods are currently used to evaluate the net present value (NPV) of a delinquent debt, and to determine how to maximize the NPV of each debt. Current analytical measures of the collectability of a delinquent debt include: behavior scores, bureau scores, and payment projection scores. Although these measures all provide some information about a delinquent debt account, they all suffer different limitations on their usefulness.
Behavior scoring is based on the activities of a delinquent credit cardholder that are visible to the card issuer. The primary source of relevant behavior information used in existing scores comes from payment information (during the time the cardholder was still paying): Has the cardholder been making minimum payments only? What is the ratio of full payments to minimum payments over the past 12 months? What is the account holder's spending-to-paying ratio? Unfortunately, behavior scoring data becomes stale by the time many collection efforts are initiated. After the authorization stream is shut down, and after the cardholder has stopped making payments, the only “transactions” posted to the account are late charges, interest charges, more interest charges, etc. These transactions are not measures of the cardholder's behavior during the debt collection process. Thus, as delinquent debt collection efforts proceed, the behavior scoring data quickly becomes outdated.
Credit bureau data provides information on what the delinquent account customer is doing elsewhere, for example, if he is delinquent on other debts as well as the current debt. However, credit bureau information also suffers from a data staleness problem due to the lag time in credit bureau information reporting. For example, it typically takes approximately four months from the date of the customer's last timely payment for the credit bureau information to indicate that something is amiss with the customer's account.
Payment projection scores are used to estimate the likelihood that payments will eventually be made. These models are used in prioritizing collection cases to be worked. Currently available payment projection models rely on masterfile information, which typically contains information such as the account holder's name, address, social security number, and monthly balances. A variety of calculated quantities are generated from the masterfile. For instance, the 3-cycles rolling average balance may be calculated, or the sum of payments in the last 6 cycles as a percentage of the amount due in the last 6 cycles or percentage of the balance that is cash may be calculated. However, a problem with these variables is that there is no updating of these characteristics throughout the collection process. The same projections—only updated for the time that has passed—will be produced on day 120 as on day 30. Thus, there is no way for the payment projection score model to take advantage of information that is gleaned during the collection process itself. Furthermore, none of these currently existing measures of information about delinquent debt accounts provides information about the collection actions that will be most effective when used on a particular account. There is a wide variety of collection actions that can be taken, such as a letter, a phone call, or the sale of the debt to a collection agency. Typically, individual collectors review the delinquent accounts and select which accounts to work, and which methods to apply, based upon their previous collection experiences. However, this individualized method for evaluating collection efforts does not provide an automated and consistent method for evaluating collection actions among a group of delinquent debts.
Individually, collection specialists often rely on information contained in the account notes made by previous collectors to determine the recent actions taken on an account, such as letters sent and phone calls made. Additionally, account notes also often contain information about why the debtor has not paid; for example, he lost his job or she has been ill. Collection notes information is useful in deciding how best to work the account; for example, once a debtor tells creditors he has lost his job, the next collection specialist can call and inquire as to whether the debtor has found a new job yet. In later delinquency stages once the account has been shut off, collection notes may be the most current information about the account, and therefore collection specialists currently use this information in an individual capacity. However, because the collection notes are in text format, existing analytical methods are not able to quantify them.
What is needed is an improved method for analyzing delinquent debt accounts that uses available information about a debt holder to evaluate the likelihood of collecting on a delinquent debt. The method should also be able to evaluate the effectiveness of different collection actions, and use the information found in collector's notes as well.